Marco Polo Castillo-Villalba , Yair Romero , Pedro Miramontes
{"title":"利用monoids整合基因组和代谢参数,预测调节级联中的表型。","authors":"Marco Polo Castillo-Villalba , Yair Romero , Pedro Miramontes","doi":"10.1016/j.biosystems.2025.105595","DOIUrl":null,"url":null,"abstract":"<div><div>Molecular network modeling requires the use of mathematical and computational formalisms for a robust and accurate prediction of phenotypes. Furthermore, there is a need to extend these formalisms to be applied to large-scale molecular networks, thus helping in the understanding of biological complexity. In this work, we propose an extension of the modeling framework known as design principles, developed by M.A. Savageau, which is based on power-law modeling. While power-law modeling has proven valuable for understanding various properties of molecular networks, it does not allow for the inference of kinetic orders, which are typically associated with the number of binding sites present in molecules. We address this limitation by modifying the traditional approach to incorporate the use of monoids, thereby introducing a novel methodological framework we call Genotype Arithmetic. The resulting combinatorial object defines a family of geometric points, whose fixed points in the exponent space impose a set of constraints that determine the appropriate kinetic order to be used in the power law models. This feature enables a prediction of the binding strength and/or the number of DNA binding sites in regulatory sequences, as well as the reaction orders in enzymatic kinetics. To demonstrate the applicability of the present approach, we illustrated how the number of binding sites can be approximated in metabolic pathways formed by 3 to 9 reactions, in allosteric systems of end-product inhibition with intermediate catalytic reactions and one gene inhibition.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"257 ","pages":"Article 105595"},"PeriodicalIF":1.9000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using monoids for the integration of genomic and metabolic parameters in the prediction of phenotypes in regulatory cascades\",\"authors\":\"Marco Polo Castillo-Villalba , Yair Romero , Pedro Miramontes\",\"doi\":\"10.1016/j.biosystems.2025.105595\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Molecular network modeling requires the use of mathematical and computational formalisms for a robust and accurate prediction of phenotypes. Furthermore, there is a need to extend these formalisms to be applied to large-scale molecular networks, thus helping in the understanding of biological complexity. In this work, we propose an extension of the modeling framework known as design principles, developed by M.A. Savageau, which is based on power-law modeling. While power-law modeling has proven valuable for understanding various properties of molecular networks, it does not allow for the inference of kinetic orders, which are typically associated with the number of binding sites present in molecules. We address this limitation by modifying the traditional approach to incorporate the use of monoids, thereby introducing a novel methodological framework we call Genotype Arithmetic. The resulting combinatorial object defines a family of geometric points, whose fixed points in the exponent space impose a set of constraints that determine the appropriate kinetic order to be used in the power law models. This feature enables a prediction of the binding strength and/or the number of DNA binding sites in regulatory sequences, as well as the reaction orders in enzymatic kinetics. To demonstrate the applicability of the present approach, we illustrated how the number of binding sites can be approximated in metabolic pathways formed by 3 to 9 reactions, in allosteric systems of end-product inhibition with intermediate catalytic reactions and one gene inhibition.</div></div>\",\"PeriodicalId\":50730,\"journal\":{\"name\":\"Biosystems\",\"volume\":\"257 \",\"pages\":\"Article 105595\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosystems\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0303264725002059\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0303264725002059","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
Using monoids for the integration of genomic and metabolic parameters in the prediction of phenotypes in regulatory cascades
Molecular network modeling requires the use of mathematical and computational formalisms for a robust and accurate prediction of phenotypes. Furthermore, there is a need to extend these formalisms to be applied to large-scale molecular networks, thus helping in the understanding of biological complexity. In this work, we propose an extension of the modeling framework known as design principles, developed by M.A. Savageau, which is based on power-law modeling. While power-law modeling has proven valuable for understanding various properties of molecular networks, it does not allow for the inference of kinetic orders, which are typically associated with the number of binding sites present in molecules. We address this limitation by modifying the traditional approach to incorporate the use of monoids, thereby introducing a novel methodological framework we call Genotype Arithmetic. The resulting combinatorial object defines a family of geometric points, whose fixed points in the exponent space impose a set of constraints that determine the appropriate kinetic order to be used in the power law models. This feature enables a prediction of the binding strength and/or the number of DNA binding sites in regulatory sequences, as well as the reaction orders in enzymatic kinetics. To demonstrate the applicability of the present approach, we illustrated how the number of binding sites can be approximated in metabolic pathways formed by 3 to 9 reactions, in allosteric systems of end-product inhibition with intermediate catalytic reactions and one gene inhibition.
期刊介绍:
BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.